In [1]:

    

# As usual, a bit of setup

import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.cnn import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient
from cs231n.layers import *
from cs231n.fast_layers import *
from cs231n.solver import Solver

%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'

# for auto-reloading external modules
# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython
%load_ext autoreload
%autoreload 2
                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        
def rel_error(x, y):
  """ returns relative error """
  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))


    

In [ ]:

    

f = h5py.File('/home/zengliang/x_train.hdf5','r')
x_train = f['x_train'][:]
f.close()

f = h5py.File('/home/zengliang/y_train.hdf5','r')
y_train = f['y_train'][:]
f.close()

f = h5py.File('/home/zengliang/x_val.hdf5','r')
x_val = f['x_val'][:]
f.close()

f = h5py.File('/home/zengliang/y_val.hdf5','r')
y_val = f['y_val'][:]
f.close()

f = h5py.File('/home/zengliang/x_test.hdf5','r')
x_test = f['x_test'][:]
f.close()

f = h5py.File('/home/zengliang/y_test.hdf5','r')
y_test = f['y_test'][:]
f.close()